Optimal Electricity Load Interruption Based on Time Series Classification With Super Learner and Feature Filtering

Load-shedding is vital for managing electrical power shortages and avoiding grid collapse. However, excessive electricity demand poses an imminent threat to the overall stability of power grid system (PGS) and its ability to run safely and reliably. Load-shedding strategies can be complicated and inadequate to manage electrical power system efficiently. The study proposed a data-driven load-shedding time series classification (TSC) technique employing a heterogeneous ensemble super learner (eSL) to categorize load-shedding based on contributing features. The model investigated challenges with binary classification while using a multidimensional time series for South Africa’s hourly load-shedding stages in MWcollected from PGS data. Considering that load-shedding is planned and predicted based on contributing features, we use these features as strong indicators to classify expected outcomes for load-shedding or no load-shedding. Validation tests for the suggested technique included the precision recall curve, the confusion matrix, the class likelihood ratio, the Brier skill scores and critical difference factor (CDF). Logistic regression (LR) produced the highest CDF average score, while support vector classifier (SVC) had the highest balanced precision (90.694%). The recursive feature elimination (RFE) model exhibited the most significant true negative and true positive counts, at 50.59% and 40.84%, respectively, and the highest proportion of valid classifications.